{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T00:23:07Z","timestamp":1759623787005,"version":"build-2065373602"},"reference-count":20,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"10","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Fundamentals"],"published-print":{"date-parts":[[2025,10,1]]},"DOI":"10.1587\/transfun.2025eap1021","type":"journal-article","created":{"date-parts":[[2025,4,6]],"date-time":"2025-04-06T18:06:37Z","timestamp":1743962797000},"page":"1393-1400","source":"Crossref","is-referenced-by-count":0,"title":["HC-HGCN: A Human-Computer Collaborative Diagnostic Model Based on Hypergraph Convolutional Neural Network for Pulmonary Nodule Imaging"],"prefix":"10.1587","volume":"E108.A","author":[{"given":"Xi","family":"DING","sequence":"first","affiliation":[{"name":"School of Medical Imaging, Binzhou Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"LI","sequence":"additional","affiliation":[{"name":"The Institute of Computing Science and Technology, Guangzhou University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunyu","family":"LIU","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Xidian University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuguang","family":"XU","sequence":"additional","affiliation":[{"name":"Institute of Medical Artificial Intelligence, Binzhou Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofeng","family":"WU","sequence":"additional","affiliation":[{"name":"School of Medical Imaging, Binzhou Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peiyuan","family":"WANG","sequence":"additional","affiliation":[{"name":"Department of Radiology, Yantai Affiliated Hospital of Binzhou Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhuoqun","family":"WANG","sequence":"additional","affiliation":[{"name":"Institute of Medical Artificial Intelligence, Binzhou Medical University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"publisher","unstructured":"[1] Y. Chen, X. Hou, Y. Yang, Q. Ge, Y. Zhou, and S. Nie, \u201cA novel deep learning model based on multi-scale and multi-view for detection of pulmonary nodules,\u201d J. Digit. Imaging, vol.36, no.2, pp.688-699, Dec. 2022. 10.1007\/s10278-022-00749-x","DOI":"10.1007\/s10278-022-00749-x"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] D. Painuli, S. Bhardwaj, and U. k\u00f6se, \u201cRecent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review,\u201d Comput. Biol. Med., vol.146, p.105580, July 2022. 10.1016\/j.compbiomed.2022.105580","DOI":"10.1016\/j.compbiomed.2022.105580"},{"key":"3","doi-asserted-by":"publisher","unstructured":"[3] X. Zhang, P. Yang, J. Tian, F. Wen, X. Chen, and T. Muhammad, \u201cClassification of benign and malignant pulmonary nodule based on local-global hybrid network,\u201d Journal of X-Ray Science and Technology: Clinical Applications of Diagnosis and Therapeutics, vol.32, no.3, pp.689-706, May 2024. 10.3233\/xst-230291","DOI":"10.3233\/XST-230291"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] H. Zhu, W. Liu, Z. Gao, and H. Zhang, \u201cExplainable classification of benign-malignant pulmonary nodules with neural networks and information bottleneck,\u201d IEEE Trans. Neural Netw. Learning Syst., vol.36, no.2, pp.2028-2039, 2025. 10.1109\/tnnls.2023.3303395","DOI":"10.1109\/TNNLS.2023.3303395"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] J. Shao, L. Zhou, S.Y. F. Yeung, T. Lei, W. Zhang, and X. Yuan, \u201cPulmonary nodule detection and classification using all-optical deep diffractive neural network,\u201d Life, vol.13, no.5, p.1148, May 2023. 10.3390\/life13051148","DOI":"10.3390\/life13051148"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] K. Cao, H. Tao, Z. Wang, and X. Jin, \u201cMSM-ViT: A multi-scale MobileViT for pulmonary nodule classification using CT images,\u201d J. Xray Sci. Technol., vol.31, no.4, pp.731-744, July 2023.","DOI":"10.3233\/XST-230014"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. He, B. Wang, J. Tao, Q. Liu, M. Peng, S. Xiong, J. Li, B. Cheng, C. Li, S. Jiang, X. Qiu, Y. Yang, Z. Ye, F. Zeng, J. Zhang, D. Liu, W. Li, Z. Chen, Q. Zeng, J.-B. Fan, and W. Liang, \u201cAccurate classification of pulmonary nodules by a combined model of clinical, imaging, and cell-free DNA methylation biomarkers: a model development and external validation study,\u201d The Lancet Digital Health, vol.5, no.10, pp.e647-e656, Oct. 2023.","DOI":"10.1016\/S2589-7500(23)00125-5"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] L. Sun, M. Zhang, Y. Lu, W. Zhu, Y. Yi, and F. Yan, \u201cNodule-CLIP: Lung nodule classification based on multi-modal contrastive learning,\u201d Computers in Biology and Medicine, vol.175, p.108505, June 2024. 10.1016\/j.compbiomed.2024.108505","DOI":"10.1016\/j.compbiomed.2024.108505"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] T. Li, J. Mao, J. Yu, Z. Zhao, M. Chen, Z. Yao, L. Fang, and B. Hu, \u201cFully automated classification of pulmonary nodules in positron emission tomography-computed tomography imaging using a two-stage multimodal learning approach,\u201d Quant. Imaging Med. Surg., vol.14, no.8, pp.5526-5540, Aug. 2024. 10.21037\/qims-24-234","DOI":"10.21037\/qims-24-234"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] A.M.D. Wolf, K.C. Oeffinger, T.Y. Shih, L.C. Walter, T.R. Church, E.T.H. Fontham, E.B. Elkin, R.D. Etzioni, C.E. Guerra, R.B. Perkins, K.K. Kondo, T.B. Kratzer, D. Manassaram-Baptiste, W.L. Dahut, and R.A. Smith, \u201cScreening for lung cancer: 2023 guideline update from the American Cancer Society,\u201d CA Cancer J. Clin., vol.74, no.1, pp.50-81, Jan. 2024. 10.3322\/caac.21811","DOI":"10.3322\/caac.21811"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] F.C. Detterbeck, G.A. Woodard, A.S. Bader, S. Dacic, M.J. Grant, H.S. Park, and L.T. Tanoue, \u201cThe proposed ninth edition TNM classification of lung cancer,\u201d CHEST, vol.166, no.4, pp.882-895, Oct. 2024. 10.1016\/j.chest.2024.05.026","DOI":"10.1016\/j.chest.2024.05.026"},{"key":"12","unstructured":"[12] A.P. Reeves and A.M. Biancardi, \u201cThe Lung Image Database Consortium (LIDC) Nodule Size Report,\u201d http:\/\/www.via.cornell.edu\/lidc\/, accessed: May 20, 2024."},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d arXiv:1512.03385, Dec. 2015. 10.48550\/arXiv.1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] S. Woo, J. Park, J.-Y. Lee, and I.S. Kweon, \u201cCBAM: Convolutional block attention module,\u201d Proc. 15th European Conf. on Computer Vision (ECCV 2018), Munich, Germany, pp.3-19, Sept. 2018. 10.1007\/978-3-030-01234-2_1","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] Y. Gao, Z. Zhang, H. Lin, X. Zhao, S. Du, and C. Zou, \u201cHypergraph learning: Methods and practices,\u201d IEEE Trans. Pattern Anal. Mach. Intell., vol.44, no.5, pp.2548-2566, 2022. 10.1109\/TPAMI.2020.3039374","DOI":"10.1109\/TPAMI.2020.3039374"},{"key":"16","unstructured":"[16] A. Canziani, A. Paszke, and E. Culurciello, \u201cAn analysis of deep neural network models for practical applications,\u201d arXiv:1605.07678, April 2017. 10.48550\/arXiv.1605.07678"},{"key":"17","unstructured":"[17] M. Tan and Q.V. Le, \u201cEfficientNet: Rethinking model scaling for convolutional neural networks,\u201d arXiv:1905.11946, 2019. 10.48550\/arXiv.1905.11946"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, \u201cA ConvNet for the 2020s,\u201d arXiv:2201.03545, March 2022. 10.48550\/arXiv.2201.03545","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] L. Liu, Q. Dou, H. Chen, J. Qin, and P.-A. Heng, \u201cMulti-task deep model with margin ranking loss for lung nodule analysis,\u201d IEEE Trans. Med. Imag., vol.39, no.3, pp.718-728, March 2020. 10.1109\/TMI.2019.2934577","DOI":"10.1109\/TMI.2019.2934577"},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] P. Shi, W. Yu, Y. Liu, and Z. Qin, \u201cDual convolutional neural network for lung nodule classification,\u201d 2021 International Joint Conference on Neural Networks (IJCNN), Shenzhen, China, pp.1-7, July 2021. 10.1109\/IJCNN52387.2021.9533336","DOI":"10.1109\/IJCNN52387.2021.9533336"}],"container-title":["IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/10\/E108.A_2025EAP1021\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T03:30:00Z","timestamp":1759548600000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transfun\/E108.A\/10\/E108.A_2025EAP1021\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"references-count":20,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transfun.2025eap1021","relation":{},"ISSN":["0916-8508","1745-1337"],"issn-type":[{"type":"print","value":"0916-8508"},{"type":"electronic","value":"1745-1337"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"article-number":"2025EAP1021"}}